code: def gather_features(
      image_features,
      text_features,
      local_loss=False,
      gather_with_grad=False,
      rank=0,
      world_size=1,
      use_horovod=False,
  ):
      """
      Gather features across GPUs.
  
      Parameters
      ----------
      image_features
          image features of the current process.
      text_features
          text features of the current process.
      local_loss
          If False, make sure the features on the current GPU have gradients.
      gather_with_grad
          Whether to gather all features with gradients enabled.
      rank
          Rank of the current process (it should be a number between 0 and world_size-1).
      world_size
          Number of processes participating in the job.
      use_horovod
          Whether to use horovod.
  
      Returns
      -------
      Gathered image and text features from all processes.
      """
      assert (
          has_distributed
      ), "torch.distributed did not import correctly, please use a PyTorch version with support."
      if use_horovod:
          assert hvd is not None, "Please install horovod"
          if gather_with_grad:
              all_image_features = hvd.allgather(image_features)
              all_text_features = hvd.allgather(text_features)
          else:
              with torch.no_grad():
                  all_image_features = hvd.allgather(image_features)
                  all_text_features = hvd.allgather(text_features)
              if not local_loss:
                  # ensure grads for local rank when all_* features don't have a gradient
                  gathered_image_features = list(
                      all_image_features.chunk(world_size, dim=0)
                  )
                  gathered_text_features = list(
                      all_text_features.chunk(world_size, dim=0)
                  )
                  gathered_image_features[rank] = image_features
                  gathered_text_features[rank] = text_features
                  all_image_features = torch.cat(
                      gathered_image_features, dim=0
                  )
                  all_text_features = torch.cat(
                      gathered_text_features, dim=0
                  )
      else:
          # We gather tensors from all gpus
          if gather_with_grad:
              all_image_features = torch.cat(
                  torch.distributed.nn.all_gather(image_features), dim=0
              )
              all_text_features = torch.cat(
                  torch.distributed.nn.all_gather(text_features), dim=0
              )
          else:
              gathered_image_features = [
                  torch.zeros_like(image_features)
                  for _ in range(world_size)
              ]
              gathered_text_features = [
                  torch.zeros_like(text_features)
                  for _ in range(world_size)
              ]
              dist.all_gather(gathered_image_features, image_features)
              dist.all_gather(gathered_text_features, text_features)
              if not local_loss:
                  # ensure grads for local rank when all_* features don't have a gradient
                  gathered_image_features[rank] = image_features
                  gathered_text_features[rank] = text_features
              all_image_features = torch.cat(
                  gathered_image_features, dim=0
              )
              all_text_features = torch.cat(
                  gathered_text_features, dim=0
              )
  
      return all_image_features, all_text_features
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: gather_features(image_features, text_features, local_loss, gather_with_grad, rank, world_size, use_horovod) and returns: all_image_features,  all_text_features